Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences

He Zhao, Piyush Rai, Lan Du, Wray Buntine
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:1943-1951, 2018.

Abstract

We present a probabilistic, fully Bayesian framework for multi-label learning. Our framework is based on the idea of learning a joint low-rank embedding of the label matrix and the label co-occurrence matrix. The proposed framework has the following appealing aspects: (1) It leverages the sparsity in the label matrix and the feature matrix, which results in very efficient inference, especially for sparse datasets, commonly encountered in multi-label learning problems, and (2) By effectively utilizing the label co-occurrence information, the model yields improved prediction accuracies, especially in the case where the amount of training data is low and/or the label matrix has a significant fraction of missing labels. Our framework enjoys full local conjugacy and admits a simple inference procedure via a scalable Gibbs sampler. We report experimental results on a number of benchmark datasets, on which it outperforms several state-of-the-art multi-label learning models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-zhao18b, title = {Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences}, author = {Zhao, He and Rai, Piyush and Du, Lan and Buntine, Wray}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {1943--1951}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/zhao18b/zhao18b.pdf}, url = {https://proceedings.mlr.press/v84/zhao18b.html}, abstract = {We present a probabilistic, fully Bayesian framework for multi-label learning. Our framework is based on the idea of learning a joint low-rank embedding of the label matrix and the label co-occurrence matrix. The proposed framework has the following appealing aspects: (1) It leverages the sparsity in the label matrix and the feature matrix, which results in very efficient inference, especially for sparse datasets, commonly encountered in multi-label learning problems, and (2) By effectively utilizing the label co-occurrence information, the model yields improved prediction accuracies, especially in the case where the amount of training data is low and/or the label matrix has a significant fraction of missing labels. Our framework enjoys full local conjugacy and admits a simple inference procedure via a scalable Gibbs sampler. We report experimental results on a number of benchmark datasets, on which it outperforms several state-of-the-art multi-label learning models.} }
Endnote
%0 Conference Paper %T Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences %A He Zhao %A Piyush Rai %A Lan Du %A Wray Buntine %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-zhao18b %I PMLR %P 1943--1951 %U https://proceedings.mlr.press/v84/zhao18b.html %V 84 %X We present a probabilistic, fully Bayesian framework for multi-label learning. Our framework is based on the idea of learning a joint low-rank embedding of the label matrix and the label co-occurrence matrix. The proposed framework has the following appealing aspects: (1) It leverages the sparsity in the label matrix and the feature matrix, which results in very efficient inference, especially for sparse datasets, commonly encountered in multi-label learning problems, and (2) By effectively utilizing the label co-occurrence information, the model yields improved prediction accuracies, especially in the case where the amount of training data is low and/or the label matrix has a significant fraction of missing labels. Our framework enjoys full local conjugacy and admits a simple inference procedure via a scalable Gibbs sampler. We report experimental results on a number of benchmark datasets, on which it outperforms several state-of-the-art multi-label learning models.
APA
Zhao, H., Rai, P., Du, L. & Buntine, W.. (2018). Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:1943-1951 Available from https://proceedings.mlr.press/v84/zhao18b.html.

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